A Statistical Physiological-Model-Constrained Framework for Computational Imaging of Subject-Specific Volumetric Cardiac Electrophysiology Using Optical Imaging and MRI Data

Computational imaging of personalized cardiac electrophysiology has attracted increasing research interest because of its clinical relevance in aiding in the diagnosis and prediction of cardiac electrical malfunctions of individual subjects. We have developed a statistical physiological-model-constrained framework that, rather than delivering a personalized cardiac electrophysiological model with customized parameters, uses simple standard electrophysiological models as constraints and produces maximum a posteriori estimation of three-dimensionally distributed transmembrane potential (TMP) dynamics inside the ventricular myocardiurn of individual subjects [1]. Taking part in 2010 Cardiac Electrophysiological Simulation Challenge (CESC'10), we modify this framework to use epicardial optical mapping data to estimate subject-specific TMP dynamics inside the 3D myocardium. Results of estimated dynamics are compared to the simulations by the same electrophysiological model with standard or adjusted parameters. As shown, while it is rather challenging to personalize the parameters of a cardiac electrophysiological model for the entire 3D myocardium, because of the drastically simplified model structure and limited subject's data, the presented approach of TMP estimation is able to computationally reproduce subject-specific electrical functions inside the 3D myocardium with simple standard model as constraints.